Multi-stages genetic algorithms: Introducing temporal structures to facilitate selection of optimal evolutionary paths

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Standard genetic algorithms (GA) are often confronted with the problem of rapid premature convergence. The loss of diversity in a population usually slows down evolution to a significant extent. In this paper, we explore the use of an original strategy called the Multi-stages GA as a means of impeding premature convergence and optimizing evolutionary progresses at the same time. The algorithm introduces the idea of temporally organizing an evolutionary process. Evaluation results show that the Multi-stages GA significantly outperforms the standard GA.

Original languageEnglish (US)
Title of host publicationProceedings - 6th International Conference on Machine Learning and Applications, ICMLA 2007
Pages56-61
Number of pages6
DOIs
StatePublished - Dec 1 2007
Externally publishedYes
Event6th International Conference on Machine Learning and Applications, ICMLA 2007 - Cincinnati, OH, United States
Duration: Dec 13 2007Dec 15 2007

Publication series

NameProceedings - 6th International Conference on Machine Learning and Applications, ICMLA 2007

Conference

Conference6th International Conference on Machine Learning and Applications, ICMLA 2007
CountryUnited States
CityCincinnati, OH
Period12/13/0712/15/07

All Science Journal Classification (ASJC) codes

  • Computer Science Applications
  • Human-Computer Interaction
  • Control and Systems Engineering

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  • Cite this

    Qian, T. (2007). Multi-stages genetic algorithms: Introducing temporal structures to facilitate selection of optimal evolutionary paths. In Proceedings - 6th International Conference on Machine Learning and Applications, ICMLA 2007 (pp. 56-61). [4457208] (Proceedings - 6th International Conference on Machine Learning and Applications, ICMLA 2007). https://doi.org/10.1109/ICMLA.2007.77